{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/generate-indices","entry":"generate_indices","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":5,"n_papers_ran":4,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":4,"n_samples_ran":2,"n_samples_fingerprinted":1,"n_places":6,"n_places_pointer_only":2,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":2},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2505.16620","paper":"/paper/causaldynamics-a-large-scale-benchmark-for","title":"CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal models","date":"2025-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kausable/CausalDynamics","path":"src/causaldynamics/baselines/cuts.py","file_url":"https://github.com/kausable/CausalDynamics/blob/HEAD/src/causaldynamics/baselines/cuts.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5c86959f0283a76a","mcp_get_code":{"code_sha256":"5c86959f0283a76a"}},{"arxiv_id":"2404.09788","paper":"/paper/shape-arithmetic-expressions-advancing","title":"Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations","date":"2024-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"krzysztof-kacprzyk/shares","path":"experiments/benchmarks_2.py","file_url":"https://github.com/krzysztof-kacprzyk/shares/blob/HEAD/experiments/benchmarks_2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4850e0530dde79d5","mcp_get_code":{"code_sha256":"4850e0530dde79d5"}},{"arxiv_id":"2305.05890","paper":"/paper/cuts-high-dimensional-causal-discovery-from","title":"CUTS+: High-dimensional Causal Discovery from Irregular Time-series","date":"2023-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"5c86959f0283a76a","mcp_get_code":{"code_sha256":"5c86959f0283a76a"}},{"arxiv_id":"2302.07458","paper":"/paper/cuts-neural-causal-discovery-from-irregular","title":"CUTS: Neural Causal Discovery from Irregular Time-Series Data","date":"2023-02-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jarrycyx/unn","path":"CUTS_Plus/cuts_plus.py","file_url":"https://github.com/jarrycyx/unn/blob/HEAD/CUTS_Plus/cuts_plus.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5c86959f0283a76a","mcp_get_code":{"code_sha256":"5c86959f0283a76a"}},{"arxiv_id":"2008.05742","paper":"/paper/skeletonnet-a-topology-preserving-solution","title":"SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images","date":"2020-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tangjiapeng/SkeletonNet","path":"sharedata/voxel2layer.py","file_url":"https://github.com/tangjiapeng/SkeletonNet/blob/HEAD/sharedata/voxel2layer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0b816a02ad9db4d5","mcp_get_code":{"code_sha256":"0b816a02ad9db4d5"}},{"arxiv_id":"2008.05742","paper":"/paper/skeletonnet-a-topology-preserving-solution","title":"SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images","date":"2020-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tangjiapeng/SkeletonNet","path":"sharedata/voxel2layer_torch.py","file_url":"https://github.com/tangjiapeng/SkeletonNet/blob/HEAD/sharedata/voxel2layer_torch.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c9b4e8f09bbdf20d","mcp_get_code":{"code_sha256":"c9b4e8f09bbdf20d"}}]}